OpenRaiser/Intern-Atlas huggingface dataset:
Batch-normalized Maxout Network in Network
Venue: arXiv cs.CV 2015
Year: 2015

This paper reports a novel deep architecture referred to as Maxout network In Network (MIN), which can enhance model discriminability and facilitate the process of information abstraction within the receptive field. The proposed network adopts the framework of the recently developed Network In Network structure, which slides a universal approximator, multilayer perceptron (MLP) with rectifier units, to exact features. Instead of MLP, we employ maxout MLP to learn a variety of piecewise linear activation functions and to mediate the problem of vanishing gradients that can occur when using rectifier units. Moreover, batch normalization is applied to reduce the saturation of maxout units by pre-conditioning the model and dropout is applied to prevent overfitting. Finally, average pooling is used in all pooling layers to regularize maxout MLP in order to facilitate information abstraction in every receptive field while tolerating the change of object position. Because average pooling preserves all features in the local patch, the proposed MIN model can enforce the suppression of irrelevant information during training. Our experiments demonstrated the state-of-the-art classification performance when the MIN model was applied to MNIST, CIFAR-10, and CIFAR-100 datasets and comparable performance for SVHN dataset.
Distributed Compressive Sensing Based Doubly Selective Channel Estimation for Large-Scale MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Doubly selective (DS) channel estimation in largescale multiple-input multiple-output (MIMO) systems is a challenging problem due to the requirement of unaffordable pilot overheads and prohibitive complexity. In this paper, we propose a novel distributed compressive sensing (DCS) based channel estimation scheme to solve this problem. In the scheme, we introduce the basis expansion model (BEM) to reduce the required channel coefficients and pilot overheads. And due to the common sparsity of all the transmit-receive antenna pairs in delay domain, we estimate the BEM coefficients by considering the DCS framework, which has a simple linear structure with low complexity. Further more, a linear smoothing method is proposed to improve the estimation accuracy. Finally, we conduct various simulations to verify the validity of the proposed scheme and demonstrate the performance gains of the proposed scheme compared with conventional schemes.
Exploiting Egocentric Object Prior for 3D Saliency Detection
Venue: arXiv cs.CV 2015
Year: 2015

On a minute-to-minute basis people undergo numerous fluid interactions with objects that barely register on a conscious level. Recent neuroscientific research demonstrates that humans have a fixed size prior for salient objects. This suggests that a salient object in 3D undergoes a consistent transformation such that people's visual system perceives it with an approximately fixed size. This finding indicates that there exists a consistent egocentric object prior that can be characterized by shape, size, depth, and location in the first person view. In this paper, we develop an EgoObject Representation, which encodes these characteristics by incorporating shape, location, size and depth features from an egocentric RGBD image. We empirically show that this representation can accurately characterize the egocentric object prior by testing it on an egocentric RGBD dataset for three tasks: the 3D saliency detection, future saliency prediction, and interaction classification. This representation is evaluated on our new Egocentric RGBD Saliency dataset that includes various activities such as cooking, dining, and shopping. By using our EgoObject representation, we outperform previously proposed models for saliency detection (relative 30% improvement for 3D saliency detection task) on our dataset. Additionally, we demonstrate that this representation allows us to predict future salient objects based on the gaze cue and classify people's interactions with objects.
MUSIC for multidimensional spectral estimation: stability and super-resolution
Venue: arXiv cs.IT 2015
Year: 2015

This paper presents a performance analysis of the MUltiple SIgnal Classification (MUSIC) algorithm applied on $D$ dimensional single-snapshot spectral estimation while $s$ true frequencies are located on the continuum of a bounded domain. Inspired by the matrix pencil form, we construct a D-fold Hankel matrix from the measurements and exploit its Vandermonde decomposition in the noiseless case. MUSIC amounts to identifying a noise subspace, evaluating a noise-space correlation function, and localizing frequencies by searching the $s$ smallest local minima of the noise-space correlation function. In the noiseless case, $(2s)^D$ measurements guarantee an exact reconstruction by MUSIC as the noise-space correlation function vanishes exactly at true frequencies. When noise exists, we provide an explicit estimate on the perturbation of the noise-space correlation function in terms of noise level, dimension $D$, the minimum separation among frequencies, the maximum and minimum amplitudes while frequencies are separated by two Rayleigh Length (RL) at each direction. As a by-product the maximum and minimum non-zero singular values of the multidimensional Vandermonde matrix whose nodes are on the unit sphere are estimated under a gap condition of the nodes. Under the 2-RL separation condition, if noise is i.i.d. gaussian, we show that perturbation of the noise-space correlation function decays like $\sqrt{\log(\#(\mathbf{N}))/\#(\mathbf{N})}$ as the sample size $\#(\mathbf{N})$ increases. When the separation among frequencies drops below 2 RL, our numerical experiments show that the noise tolerance of MUSIC obeys a power law with the minimum separation of frequencies.
Cooperative Non-Orthogonal Multiple Access with Simultaneous Wireless Information and Power Transfer
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, the application of simultaneous wireless information and power transfer (SWIPT) to non-orthogonal multiple access (NOMA) networks in which users are spatially randomly located is investigated. A new cooperative SWIPT NOMA protocol is proposed, in which near NOMA users that are close to the source act as energy harvesting relays to help far NOMA users. Since the locations of users have a significant impact on the performance, three user selection schemes based on the user distances from the base station are proposed. To characterize the performance of the proposed selection schemes, closed-form expressions for the outage probability and system throughput are derived. These analytical results demonstrate that the use of SWIPT will not jeopardize the diversity gain compared to the conventional NOMA. The proposed results confirm that the opportunistic use of node locations for user selection can achieve low outage probability and deliver superior throughput in comparison to the random selection scheme.
Visual Language Modeling on CNN Image Representations
Venue: arXiv cs.CV 2015
Year: 2015

Measuring the naturalness of images is important to generate realistic images or to detect unnatural regions in images. Additionally, a method to measure naturalness can be complementary to Convolutional Neural Network (CNN) based features, which are known to be insensitive to the naturalness of images. However, most probabilistic image models have insufficient capability of modeling the complex and abstract naturalness that we feel because they are built directly on raw image pixels. In this work, we assume that naturalness can be measured by the predictability on high-level features during eye movement. Based on this assumption, we propose a novel method to evaluate the naturalness by building a variant of Recurrent Neural Network Language Models on pre-trained CNN representations. Our method is applied to two tasks, demonstrating that 1) using our method as a regularizer enables us to generate more understandable images from image features than existing approaches, and 2) unnaturalness maps produced by our method achieve state-of-the-art eye fixation prediction performance on two well-studied datasets.
A disembodied developmental robotic agent called Samu Bátfai
Venue: arXiv cs.AI 2015
Year: 2015

The agent program, called Samu, is an experiment to build a disembodied DevRob (Developmental Robotics) chatter bot that can talk in a natural language like humans do. One of the main design feature is that Samu can be interacted with using only a character terminal. This is important not only for practical aspects of Turing test or Loebner prize, but also for the study of basic principles of Developmental Robotics. Our purpose is to create a rapid prototype of Q-learning with neural network approximators for Samu. We sketch out the early stages of the development process of this prototype, where Samu's task is to predict the next sentence of tales or conversations. The basic objective of this paper is to reach the same results using reinforcement learning with general function approximators that can be achieved by using the classical Q lookup table on small input samples. The paper is closed by an experiment that shows a significant improvement in Samu's learning when using LZW tree to narrow the number of possible Q-actions.
PCS: Predictive Component-level Scheduling for Reducing Tail Latency in Cloud Online Services
Venue: arXiv cs.DC 2015
Year: 2015

Modern latency-critical online services often rely on composing results from a large number of server components. Hence the tail latency (e.g. the 99th percentile of response time), rather than the average, of these components determines the overall service performance. When hosted on a cloud environment, the components of a service typically co-locate with short batch jobs to increase machine utilizations, and share and contend resources such as caches and I/O bandwidths with them. The highly dynamic nature of batch jobs in terms of their workload types and input sizes causes continuously changing performance interference to individual components, hence leading to their latency variability and high tail latency. However, existing techniques either ignore such fine-grained component latency variability when managing service performance, or rely on executing redundant requests to reduce the tail latency, which adversely deteriorate the service performance when load gets heavier. In this paper, we propose PCS, a predictive and component-level scheduling framework to reduce tail latency for large-scale, parallel online services. It uses an analytical performance model to simultaneously predict the component latency and the overall service performance on different nodes. Based on the predicted performance, the scheduler identifies straggling components and conducts near-optimal component-node allocations to adapt to the changing performance interferences from batch jobs. We demonstrate that, using realistic workloads, the proposed scheduler reduces the component tail latency by an average of 67.05\% and the average overall service latency by 64.16\% compared with the state-of-the-art techniques on reducing tail latency.
Investigating the stylistic relevance of adjective and verb simile markers
Venue: arXiv cs.CL 2015
Year: 2015

Similes play an important role in literary texts not only as rhetorical devices and as figures of speech but also because of their evocative power, their aptness for description and the relative ease with which they can be combined with other figures of speech (Israel et al. 2004). Detecting all types of simile constructions in a particular text therefore seems crucial when analysing the style of an author. Few research studies however have been dedicated to the study of less prominent simile markers in fictional prose and their relevance for stylistic studies. The present paper studies the frequency of adjective and verb simile markers in a corpus of British and French novels in order to determine which ones are really informative and worth including in a stylistic analysis. Furthermore, are those adjectives and verb simile markers used differently in both languages?
BOAT: a cross-platform software for data analysis and numerical computing with arbitrary-precision
Venue: arXiv cs.MS 2015
Year: 2015

BOAT is a free cross-platform software for statistical data analysis and numerical computing. Thanks to its multiple-precision floating point engine, it allows arbitrary-precision calculations, whose digits of precision are only limited by the amount of memory of the host machine. At the core of the software is a simple and efficient expression language, whose use is facilitated by the assisted typing, the auto-complete engine and the built-in help for the syntax. In this paper a quick overview of the software is given. Detailed information, together with its applications to some case studies, is available at the BOAT web page.
Dynamic Belief Fusion for Object Detection
Venue: arXiv cs.CV 2015
Year: 2015

A novel approach for the fusion of heterogeneous object detection methods is proposed. In order to effectively integrate the outputs of multiple detectors, the level of ambiguity in each individual detection score is estimated using the precision/recall relationship of the corresponding detector. The main contribution of the proposed work is a novel fusion method, called Dynamic Belief Fusion (DBF), which dynamically assigns probabilities to hypotheses (target, non-target, intermediate state (target or non-target)) based on confidence levels in the detection results conditioned on the prior performance of individual detectors. In DBF, a joint basic probability assignment, optimally fusing information from all detectors, is determined by the Dempster's combination rule, and is easily reduced to a single fused detection score. Experiments on ARL and PASCAL VOC 07 datasets demonstrate that the detection accuracy of DBF is considerably greater than conventional fusion approaches as well as individual detectors used for the fusion.
Quality of Open Source Systems from Product Metrics Perspective
Venue: arXiv cs.SE 2015
Year: 2015

Software engineering and information systems practices seek ultimately to create the flawless product. One of the tools used to improve the quality of software development is the use of metrics. In this paper, metrics retrieved from open source software were analyzed for quality attributes. Defect density is considered a strong indication of the quality of software product. Few studies have taken into consideration the density of defects while looking into quality of software and proneness to defects. Analysis of this study has shown that defect density is relevant to different developers and different product sizes. Thus, open source project has shown to have low defect density and the larger the product the lower the defect density is. In addition, this study has shown that there are different metrics that correlate with each other indicating that some of these metrics have conceptual and practical relevance to each other. Another relationship was tested between the number of bugs and the metrics. Results indicated that most attributes had positive correlation with the number of bugs with exception to coupling between cohesion among methods of class.
Online Supervised Hashing for Ever-Growing Datasets
Venue: arXiv cs.CV 2015
Year: 2015

Supervised hashing methods are widely-used for nearest neighbor search in computer vision applications. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies can be inefficient when confronted with large training datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as a dataset continues to grow and diversify over time. Yet, in many practical scenarios the dataset grows and diversifies; thus, both the hash functions and the indexing must swiftly accommodate these changes. To address these issues, we propose an online hashing method that is amenable to changes and expansions of the datasets. Since it is an online algorithm, our approach offers linear complexity with the dataset size. Our solution is supervised, in that we incorporate available label information to preserve the semantic neighborhood. Such an adaptive hashing method is attractive; but it requires recomputing the hash table as the hash functions are updated. If the frequency of update is high, then recomputing the hash table entries may cause inefficiencies in the system, especially for large indexes. Thus, we also propose a framework to reduce hash table updates. We compare our method to state-of-the-art solutions on two benchmarks and demonstrate significant improvements over previous work.
A Multilevel Framework for Lattice Network Coding
Venue: arXiv cs.IT 2015
Year: 2015

We present a general framework for studying the multilevel structure of lattice network coding (LNC), which serves as the theoretical fundamental for solving the ring-based LNC problem in practice, with greatly reduced decoding complexity. Building on the framework developed, we propose a novel lattice-based network coding solution, termed layered integer forcing (LIF), which applies to any lattices having multilevel structure. The theoretic foundations of the developed multilevel framework lead to a new general lattice construction approach, the elementary divisor construction (EDC), which shows its strength in improving the overall rate over multiple access channels (MAC) with low computational cost. We prove that the EDC lattices subsume the traditional complex construction approaches. Then a soft detector is developed for lattice network relaying, based on the multilevel structure of EDC. This makes it possible to employ iterative decoding in lattice network coding, and simulation results show the large potential of using iterative multistage decoding to approach the capacity.
Greed is Super: A Fast Algorithm for Super-Resolution
Venue: arXiv cs.IT 2015
Year: 2015

We present a fast two-phase algorithm for super-resolution with strong theoretical guarantees. Given the low-frequency part of the spectrum of a sequence of impulses, Phase I consists of a greedy algorithm that roughly estimates the impulse positions. These estimates are then refined by local optimization in Phase II. In contrast to the convex relaxation proposed by Candès et al., our approach has a low computational complexity but requires the impulses to be separated by an additional logarithmic factor to succeed. The backbone of our work is the fundamental work of Slepian et al. involving discrete prolate spheroidal wave functions and their unique properties.
Comparison of parallel sorting algorithms
Venue: arXiv cs.DC 2015
Year: 2015

In our study we implemented and compared seven sequential and parallel sorting algorithms: bitonic sort, multistep bitonic sort, adaptive bitonic sort, merge sort, quicksort, radix sort and sample sort. Sequential algorithms were implemented on a central processing unit using C++, whereas parallel algorithms were implemented on a graphics processing unit using CUDA platform. We chose these algorithms because to the best of our knowledge their sequential and parallel implementations were not yet compared all together in the same execution environment. We improved the above mentioned implementations and adopted them to be able to sort input sequences of arbitrary length. We compared algorithms on six different input distributions, which consisted of 32-bit numbers, 32-bit key-value pairs, 64-bit numbers and 64-bit key-value pairs. In this report we give a short description of seven sorting algorithms and all the results obtained by our tests.
Hierarchical Latent Semantic Mapping for Automated Topic Generation
Venue: arXiv cs.LG 2015
Year: 2015

Much of information sits in an unprecedented amount of text data. Managing allocation of these large scale text data is an important problem for many areas. Topic modeling performs well in this problem. The traditional generative models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and most recent research on topic generation has been focusing on improving or extending these models. However, results of traditional generative models are sensitive to the number of topics K, which must be specified manually. The problem of generating topics from corpus resembles community detection in networks. Many effective algorithms can automatically detect communities from networks without a manually specified number of the communities. Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus. HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network. We apply HLSM to several document collections and the experimental comparisons against several state-of-the-art approaches demonstrate the promising performance.
Automatic Measurement of Physical Mobility in Get-Up-and-Go Test Using Kinect Sensor
Venue: arXiv cs.HC 2015
Year: 2015

Get-Up-and-Go Test is commonly used for assessing the physical mobility of the elderly by physicians. This paper presents a method for automatic analysis and classification of human gait in the Get-Up-and-Go Test using a Microsoft Kinect sensor. Two types of features are automatically extracted from the human skeleton data provided by the Kinect sensor. The first type of feature is related to the human gait (e.g., number of steps, step duration, and turning duration); whereas the other one describes the anatomical configuration (e.g., knee angles, leg angle, and distance between elbows). These features characterize the degree of human physical mobility. State-of-the-art machine learning algorithms (i.e. Bag of Words and Support Vector Machines) are used to classify the severity of gaits in 12 subjects with ages ranging between 65 and 90 enrolled in a pilot study. Our experimental results show that these features can discriminate between patients who have a high risk for falling and patients with a lower fall risk.
Are Game Platforms suitable for Parkinson Disease patients?
Venue: arXiv cs.CY 2015
Year: 2015

Parkinson's Disease (PD) is a progressive neurodegenerative movement disorder that affects more that 6 million people worldwide. Motor dysfunction gradually increases as the disease progress. It is usually mild in the early stages of the disease but it relentlessly progresses to a severe or very severe disability that is characterized by increasing degrees of bradykinesia, hypokinesia, muscle rigidity, loss of postural reflexes and balance control as well as freezing of gait. In addition to a line of treatment based on dopaminergic PD-specific drugs, attending neurologists strongly recommend regular exercise combined with physiotherapy. However, the routine of traditional rehabilitation often create boredom and loss of interest. Opportunities to liven up a daily exercise schedule may well take the form of character-based virtual reality games which engage the player to physically train in a non-linear and looser fashion, providing an experience that varies from one game loop the next. Such "exergames", a word that results from the amalgamation of the words "exercise" and "game" challenge patients into performing movements of varying complexity in a playful and immersive virtual environment. In fact, today's game consoles using controllers like Nintendo's Wii, Sony PlayStation Eye and the Microsoft Kinect sensor present new opportunities to infuse motivation and variety to an otherwise mundane physiotherapy routine. But are these controllers and the games built for them appropriate for PD patients? In this paper we present some of these approaches and discuss their suitability for these patients mainly on the basis of demands made on balance, agility and gesture precision.